Indeed, multivariate time series can be generally formalized as spatial-temporal graph data [1]}.
On the one hand, multivariate time series have complex temporal patterns, e.g., multiple periodicities.
On the other hand, different time series can affect other's evolutionary processes because of the underlying interdependencies between variables, which is non-Euclidean and is reasonably modeled by the graph structure.
To illustrate, we take the traffic flow system as an example, where each sensor corresponds to a variable.
Figure REF (a) depicts the traffic flow time series generated from two sensors deployed on the road network.
Apparently, there are two repeating temporal patterns, i.e., daily and weekly periodicities.
The morning/evening peaks occur every day, while the weekdays and weekends exhibit different patterns.
Furthermore, the two time series share very similar trends because the selected sensors 20 and 301 are closely connected in the traffic network.
Consequently, accurate time series forecasting depends not only on the pattern of its temporal dimension but also on its interlinked time series.
Besides, it is worth noting that we made the above analysis on the basis of observing a sufficiently long time series.
